Identification pattern systems, such as tenprint or fingerprint identification systems, play a critical role in modern society in both criminal and civil applications. For example, criminal identification in public safety sectors is an integral part of any present day investigation. Similarly in civil applications such as credit card or personal identity fraud, print identification has become an essential part of the security process.
An automatic fingerprint identification operation normally consists of two stages. The first is the registration stage and the second is the identification stage. In the registration stage, the register's prints and personal information are enrolled and features, such as minutiae, are extracted. The personal information and the extracted features are then used to form a file record that is saved into a database for subsequent print identification. Present day automatic fingerprint identification systems may contain several hundred thousand to many millions of such file records.
In the identification stage, print features from an individual, or latent print, and personal information are extracted to form what is typically referred to as a search record. The search record is then compared with the enrolled file records in the database of the fingerprint matching system. In a typical search scenario, a search record may be compared against millions of file records that are stored in the database and a list of matched scores are generated after the matching process. Candidate records are sorted according to matched prints scores. A matched score is a measurement of the similarity of the print features of the identified search and file records. The higher the score is, the more similar the file and search records are determined to be. Thus, a top candidate is the one that has the closest match.
However, it is well known from verification tests that the top candidate may not always be the correctly matched record because the obtained prints may vary widely in quality. Smudges, individual differences in technique of the personnel who obtain the prints, equipment quality, and environmental factors may all affect print quality. To ensure higher accuracy in determining the correctly matched candidate, the search record and the top n file records from the sorted list are provided to an examiner for manual review and inspection. Once a true match is found, the identification information is provided to a user and the search print is discarded from the identification system or added to the file database according to the system requirement. If a true match is not found, a new record is created and the personal information and print features of the search record are added to the file database.
Many solutions have been proposed to improve the accuracy of matched scores and to reduce the workload of manual examiners. Most of these proposals, to date, have focused on designing improved fingerprint scanners to obtain better quality print records. Other proposals include designing a better fingerprint enhancement process and feature extraction algorithm to obtain better matching features, and designing improved matching algorithms such as the expert matcher described in U.S. Pat. No. 5,960,101. In short, more effort has been directed to improving the individual components of automatic fingerprint identification systems, and attention has been paid to developing a process to build an integrated and robust database. Thus, a system that contains poor quality search and/or file records can only provide correspondingly poor match results that may not even provide a match, even though the mated record is known to be in the database and the components of the AFIS system are improved.
Additional disadvantages of previous AFIS systems include lack of clear feedback loops between processing blocks to provide self-healing of the systems so that as time passes more searches are performed. For example the image quality measurements are normally only utilized locally to reject or accept a fingerprint. The quality measurements are not used in the matching process. The quality measurements derived are optimized with respect to using manual manipulation of the print, and are not directly optimized based on overall matching results. Previous matched results and image quality measurements are not utilized to improve future search; the number of fingers used for the match comparison is not dynamically selected based on quality of the prints; and the integrity check of prints during the registration step is not fully automated.
Thus, it would be desirable to have an improved automatic fingerprint identification system and a method that address the disadvantages enumerated above to achieve substantially increased accuracy without sacrificing speed in the identification process.
Those skilled in the art will appreciate that elements in the Figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the Figures may be exaggerated relative to other elements to help improve understanding of various embodiments of the present invention.